Impact of Client Participation Rates on Federated Multimodal VQA Performance

Abstract

Abstract: This report synthesises findings from 15 peer-reviewed papers addressing the following research question: What is the effect of varying the fraction of clients participating in each communication round on the inference efficiency and zero-shot accuracy of federated multimodal models on the VQA-v2. A significant bottleneck in federated learning (FL) is the network communication cost of sending model updates from client devices to the central server. We present a comprehensive empirical study of the statistics of model updates in FL, as well as the role and benefits of. 16 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 3.3/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the effect of varying the fraction of clients participating in each communication round on the inference efficiency and zero-shot accuracy of federated multimodal models on the VQA-v2 benchmark when using different client selection strategies?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance